With the advent of digital TV-broadcasts and video libraries presenting more than hundred of channels at a time over Antenna, Cable, Internet and Satellite, the need for a user-friendly TV-program selection is growing. Unlike the present TV and Internet, a new system should enable users to access programs clustered by genres.
There are several approaches addressing commercial-detection and video-classification. Satterwhite et al. (IEEE Potentials, pp. 9-12, 2004) describe the characteristics of commercials and give an overview of several algorithms, which have been experimentally used for detection. Usually so-called descriptor information is evaluated for detecting commercials in a video data stream. A descriptor can be considered as a filter extracting indicative parameter. A descriptor can extract commercial specific features from a video data stream.
A known descriptor refers to the appearance of several monochrome black frames also referred to as separating frames or dark frames between each commercial block. In this context Lienhart et al. published an approach (R. Lienhart et al., IEEE Conference on Multimedia Computing and Systems, pp. 509-516, 1997), requiring that the average and the standard deviation intensity values of the pixels in these frames should be below a certain threshold. Sadlier et al. (International Conference on Enterprise Information Systems, pp. 449-452, 2001) designed a method to detect black frames using the DC-coefficients in an MPEG-1-encoded bit stream.
Information on the removal of the TV-logo (network logo) during the commercial blocks is another descriptor. The recognition of logos, for example, is described in R. J. M. den Hollander et al. (International Conference on Image Processing, volume 3, pp. 517-520, 2003). These methods are computationally expensive and therefore not suitable for our real-time application.